Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-05-06"
First check the 30 states with the largest number of deaths.
## date state fips cases deaths
## 3568 2020-05-06 New York 36 329405 25956
## 3566 2020-05-06 New Jersey 34 131890 8549
## 3557 2020-05-06 Massachusetts 25 72025 4420
## 3558 2020-05-06 Michigan 26 45048 4250
## 3575 2020-05-06 Pennsylvania 42 54989 3360
## 3549 2020-05-06 Illinois 17 68164 2977
## 3541 2020-05-06 Connecticut 9 30995 2718
## 3539 2020-05-06 California 6 60787 2478
## 3554 2020-05-06 Louisiana 22 30399 2094
## 3544 2020-05-06 Florida 12 37994 1538
## 3556 2020-05-06 Maryland 24 28263 1443
## 3550 2020-05-06 Indiana 18 22286 1377
## 3545 2020-05-06 Georgia 13 29724 1309
## 3572 2020-05-06 Ohio 39 21576 1225
## 3581 2020-05-06 Texas 48 35438 985
## 3540 2020-05-06 Colorado 8 17720 919
## 3586 2020-05-06 Washington 53 16713 881
## 3585 2020-05-06 Virginia 51 20256 713
## 3569 2020-05-06 North Carolina 37 12783 497
## 3559 2020-05-06 Minnesota 27 8579 485
## 3561 2020-05-06 Missouri 29 9164 429
## 3537 2020-05-06 Arizona 4 9707 426
## 3560 2020-05-06 Mississippi 28 8424 374
## 3577 2020-05-06 Rhode Island 44 10205 370
## 3588 2020-05-06 Wisconsin 55 8901 362
## 3535 2020-05-06 Alabama 1 8691 343
## 3578 2020-05-06 South Carolina 45 6936 305
## 3553 2020-05-06 Kentucky 21 5946 286
## 3564 2020-05-06 Nevada 32 5774 286
## 3543 2020-05-06 District of Columbia 11 5461 277
For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
First check the 30 counties with the largest number of deaths.
## date county state fips cases deaths
## 119857 2020-05-06 New York City New York NA 183770 18993
## 119856 2020-05-06 Nassau New York 36059 37350 2325
## 118727 2020-05-06 Cook Illinois 17031 46689 2004
## 119390 2020-05-06 Wayne Michigan 26163 17571 1973
## 119876 2020-05-06 Suffolk New York 36103 35543 1574
## 118338 2020-05-06 Los Angeles California 6037 28644 1367
## 119782 2020-05-06 Essex New Jersey 34013 14951 1349
## 119777 2020-05-06 Bergen New Jersey 34003 16520 1289
## 119885 2020-05-06 Westchester New York 36119 30426 1285
## 119305 2020-05-06 Middlesex Massachusetts 25017 16327 1070
## 118433 2020-05-06 Fairfield Connecticut 9001 12455 952
## 119784 2020-05-06 Hudson New Jersey 34017 16197 903
## 118434 2020-05-06 Hartford Connecticut 9003 6530 842
## 120267 2020-05-06 Philadelphia Pennsylvania 42101 16697 803
## 119795 2020-05-06 Union New Jersey 34039 13604 800
## 119371 2020-05-06 Oakland Michigan 26125 7573 774
## 119787 2020-05-06 Middlesex New Jersey 34023 13254 706
## 119791 2020-05-06 Passaic New Jersey 34031 13971 690
## 119358 2020-05-06 Macomb Michigan 26099 5832 662
## 119309 2020-05-06 Suffolk Massachusetts 25025 14476 642
## 118437 2020-05-06 New Haven Connecticut 9009 8419 629
## 119307 2020-05-06 Norfolk Massachusetts 25021 6610 596
## 119301 2020-05-06 Essex Massachusetts 25009 10344 561
## 119789 2020-05-06 Morris New Jersey 34027 5655 491
## 119790 2020-05-06 Ocean New Jersey 34029 7125 483
## 120879 2020-05-06 King Washington 53033 6772 476
## 120262 2020-05-06 Montgomery Pennsylvania 42091 4827 471
## 119226 2020-05-06 Orleans Louisiana 22071 6608 464
## 118489 2020-05-06 Miami-Dade Florida 12086 13370 432
## 119303 2020-05-06 Hampden Massachusetts 25013 4321 425
For these 30 counties, I check the number of new cases and the number of new deaths.
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
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## loaded via a namespace (and not attached):
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## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
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